search for: misclassif

...this problem. "My english is bad" I hope that my question is clear: I have a matrix in wich there are two colmns( yp, yt) Yp: predicted values from my model. yt: true values ( my dependante variable y is a categorical;3 modalities (0,1,2) I don't know how to procede to calculate the misclassification rate and the error Types. Thank you for answring Doussa -- View this message in context: http://r.789695.n4.nabble.com/misclassification-rate-tp3787075p3787075.html Sent from the R help mailing list archive at Nabble.com.

Hello I want to build a classification tree for a binary response variable while the condition for the final tree should be : The total misclassification for each group (zero or one) will be less then 10% . for example: if I have in the root 100 observations, 90 from group 0 and 10 from group 1, I want that in the final tree a maximum of 9 and 1 observations out of group 0 and 1, respectively, will be misclassified. Does anyone know what code...

I have a classification tree that I computed via the tree function (in the tree package). I'd like to compute a misclassification table (if that's the right term) on the data used to compute the tree. That is, I want to compute a table with the different classes (i.e.,levels of the response factor) on the rows and the columns, and where entry [i,j] is the number of times the tree classified an observation of class...

HI Is there any function in R that tells us error rate(misclassification rate) for logistic regression type classification? i also want to know the function to determine type I and type II error. I have found a link where "misclass" and "confusion" are used. But I dont know the package name. http://alumni.media.mit.edu/~tpminka/courses/36-...

...ree.screens () # useless plot(myfit); text (myfit, all= TRUE, cex=0.5, pretty=0) # tile.tree (myfit, fgl$type) # useless # close.screen (all= TRUE) # useless My current tree plot resulted from above code shows as: 1. overlapped #s caused by unsuitable length of branch. 2. no misclassification rates: 'misclass.tree' only brings up the error of ' misclassification error rate is appropriate for factor responses only', but my response y is 0/1 data. 3. Unsuitable location of notations: there are not two notation of splitting criteria on the two branches when a node...

Hi, I've searched online, in a few books, and in the archives, but haven't seen this. I believe that xerror is scaled to rel error on the first split. After fitting an rpart object, is it possible with a little math to determine the percentage of true classifications represented by a xerror value? -seth -- View this message in context:

Hi, I am using errorest function from ipred package. I am hoping to perform "bootstrap 0.632+" and "bootstrap leave one out". According to the manual page for errorest, i use the following command: ce632[i]<-errorest(ytrain ~., data=mydata, model=lda, estimator=c("boot","632plus"), predict=mypredict.lda)$error It didn't work. I then tried the

Hello, I am pretty new to R, I have always used SAS and SAS products. My target variable is binary ('Y' and 'N') and i have about 14 predictor variables. My goal is to compare different variable selection methods like Forward, Backward, All possible subsests. I am using misclassification rate to pick the winner method. This is what i have as of now, Reg <- glm (Graduation ~., DFtrain,family=binomial(link="logit")) step <- extractAIC(Reg, direction="forward") pred <- predict(Reg, DFtest,type="response") mis <- mean({pred >...

...p. I'm using rpart to analyse data on skull base morphology, essentially predicting sex from one or several skull base measurements. The sex of the people whose skulls are being studied is known, and lives as a factor (M,F) in the data. I want to get back predictions of gender, and particularly misclassification rates. rpart produces output like this :- > printcp(rpart.LFM) Classification tree: rpart(formula = Sex ~ LFM, data = Brides2) Variables actually used in tree construction: LFM Root node error: 44/104 = 0.42308 n= 104 CP nsp...

...(model, data, probability=TRUE, decision.values = TRUE) tab<-table(predicts, data$class) tab/ This is what I face: /Parameters: SVM-Type: C-classification SVM-Kernel: linear cost: 0.26 gamma: 0.1 Number of Support Vectors: 61/ But, when I try cost=0.31, I get a lower misclassification error rate than when I get with cost=0.26 . Is this difference because the error used while tuning is different from the misclassification value? Thanks in advance. -- View this message in context: http://r.789695.n4.nabble.com/e1071-tuning-is-not-giving-the-best-within-the-range-tp46...

Hello, I would like to know how to obtain the misclassification error when performing a boosting analisis with ADABAG package? With: > prop.table(Tesis.boostcv$confusion) I obtain the confusion matrix, but not the overall missclassification error. Thanks in advance, BSc. Cecilia Lezama Facultad de Ciencias - UDELAR Montevideo - Uruguay. [[alternat...

Dear users, In my psychometric test i have applied logistic regression on my data. My data consists of 50 predictors (22 continuous and 28 categorical) plus a binary response. Using glm(), stepAIC() i didn't get satisfactory result as misclassification rate is too high. I think categorical variables are responsible for this debacle. Some of them have more than 6 level (one has 10 level). Please suggest some better regression model for this situation. If possible you can suggest some article. thanking you. Tirtha -- View this message in...

...ade of 5 different numbers. They do represent something but it would take too long to explain. I want to try and find a classification rule for the 5 numbers in the rows based on the columns so I created a classification tree and plotted that and then pruned it. My question is how do you print the misclassification rate at each node on the actual diagram of the classification tree. I can't seem to get it up there. In my notes it uses gmistext() but I have a feeling that it's for Splus rather than R as gmistext() doesn.t work for me either. Second question is when I try using the predict.tree...

Hi all - I am trying to tune an SVM model by optimizing the cross-validation accuracy. Maximizing this value doesn't necessarily seem to minimize the number of misclassifications. Can anyone tell me how the cross-validation accuracy is defined? In the output below, for example, cross-validation accuracy is 92.2%, while the number of correctly classified samples is (1476+170)/(1476+170+4) = 99.7% !? Thanks for any help. Regards - Ton --- Parameters: SVM-Type:...

...nstead I have produced a table for both analyses comparing the observed and predicted response. E.g. table(data$dependent,predict(model,type="class")) I am looking for confirmation that (a) it is incorrect to compare the error estimates for the two techniques and (b) that comparing the misclassification rates is an appropriate method for comparing the two techniques. Thanks Amy Amelia Koch University of Tasmania School of Geography and Environmental Studies Private Bag 78 Hobart Tasmania, Australia 7001 Ph: +61 3 6226 7454 ajkoch@utas.edu.au [[alternative HTML version d...

Hi users, I need to calculate .632 (and .632+) bootstrap and the cross-validation of area under curve (AUC) to compare my models. Is there any package for the same. I know about 'ipred' and using it i can calculate misclassification errors. Please help. It's urgent. -- View this message in context: http://www.nabble.com/Package-for-.632-%28and-.632%2B%29-bootstrap-and-the-cross-validation-of-ROC-Parameters-tf4068544.html#a11561405 Sent from the R help mailing list archive at Nabble.com.

...ngs, I'm using MSM (mutli-state markov modeling) package to study the progression of fibrosis in U.S hepatitis C population. I find this is a very fascinating tool for an applied researcher like myself. I have a four stage progression only model without any absorbing stage, also assuming no misclassification error in the data for the time being. I also have a couple covariates in the model. so I get these three transition rates. and I was wondering how I can compare these transition rates to be able to say that they are not equal statistically. (I guess I might be thinking too hard on this, can...

...st}, and predictor variables X, with continuous and factor variables using random forests in R. The variable Y acts like an ordinal variable, but I recoded it as factor variable. I ran a simulation and got OOB estimate of error rate 60%. I validated against some external datasets and got about 59% misclassification error. I would like to tinker with classwt option in the function randomForest to see if I can get a better performance the model. My confusion arises from how to define these weights. If I say, classwt = c(3,6,9,1,2,3), how exactly the levels get weighted. If this is a 6X6 matrix, I can put...

...turned) library(e1071) m <- naiveBayes(returned ~ ., dat) m predict(m, dat[1:5,-3]) table(predict=predict(m, dat[1:5,-3]), true=dat[1:5,3]) predict(m, dat[1:5,-3], type = "raw") So far, so good I think (???). I want to know if there is any diagnostic test to determine the overall misclassification rate of a NB classifier, and if there is a function in R that is available to implement it? Thanks, Abraham -- *Abraham Mathew Statistical Analyst www.amathew.com 720-648-0108 @abmathewks* [[alternative HTML version deleted]]

Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of...